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Journal of Arid Land
Journal of Arid Land  2022, Vol. 14 Issue (9): 941-961    DOI: 10.1007/s40333-022-0032-x
Research article     
Attribution analysis and multi-scenario prediction of NDVI drivers in the Xilin Gol grassland, China
XU Mengran, ZHANG Jing(), LI Zhenghai, MO Yu
College of Enoironment and Bioresources, Dalian Minzu University, Dalian 116600, China
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Abstract  

Grassland degradation is influenced by climate change and human activities, and has become a major obstacle for the development of arid and semi-arid areas, posing a series of environmental and socio-economic problems. An in-depth understanding of the inner relations among grassland vegetation dynamics, climate change, and human activities is therefore greatly significant for understanding the variation in regional environmental conditions and predicting future developmental trends. Based on MODIS (moderate resolution imaging spectroradiometer) NDVI (normalized difference vegetation index) data from 2000 to 2020, our objective is to investigate the spatiotemporal changes of NDVI in the Xilin Gol grassland, Inner Mongolia Autonomous Region, China. Combined with 12 natural factors and human activity factors in the same period, the dominant driving factors and their interactions were identified by using the geographic detector model, and multiple scenarios were also simulated to forecast the possible paths of future NDVI changes in this area. The results showed that: (1) in the past 21 a, vegetation cover in the Xilin Gol grassland exhibited an overall increasing trend, and the vegetation restoration (84.53%) area surpassed vegetation degradation area (7.43%); (2) precipitation, wind velocity, and livestock number were the dominant factors affecting NDVI (the explanatory power of these factors exceeded 0.4). The interaction between average annual wind velocity and average annual precipitation, and between average annual precipitation and livestock number greatly affected NDVI changes (the explanatory power of these factors exceeded 0.7). Moreover, the impact of climate change on NDVI was more significant than human activities; and (3) scenario analysis indicated that NDVI in the Xinlin Gol grassland increased under the scenarios of reduced wind velocity, increased precipitation, and ecological protection. In contrast, vegetation coverage restoration in this area was significantly reduced under the scenarios of unfavorable climate conditions and excessive human activities. This study provides a scientific basis for future vegetation restoration and management, ecological environmental construction, and sustainable natural resource utilization in this area.

Key words： normalized difference vegetation index (NDVI)      grassland degradation      geographical detector      Cellular Automat (CA)-Markov model      Xilin Gol grassland     
Received: 04 June 2022      Published: 30 September 2022
Corresponding Authors: *ZHANG Jing (E-mail: Zhangjing@dlnu.edu.cn)
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XU Mengran
ZHANG Jing
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MO Yu
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XU Mengran, ZHANG Jing, LI Zhenghai, MO Yu. Attribution analysis and multi-scenario prediction of NDVI drivers in the Xilin Gol grassland, China. Journal of Arid Land, 2022, 14(9): 941-961.

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http://jal.xjegi.com/10.1007/s40333-022-0032-x     OR     http://jal.xjegi.com/Y2022/V14/I9/941

Fig. 1 Mean NDVI (normalized difference vegetation index) in the Xilin Gol grassland during 2000-2020
Data type Data content Code Sequence length/year Source Processing method
Remote sensing MODIS NDVI NDVI 2000-2020 https://ladsweb.modaps.eosdis.nasa.gov/search HANTS (harmonic analysis of time series) for NDVI filtering in environment for visualizing images (ENVI) v.5.3
Climate Temperature (℃) TEM 2000-2020 http://data.cma.cn/ The annual average temperature was queried by (structured query language) SQL, and the Kriging interpolation was performed in ArcGIS v.10.3
Precipitation (mm) PRE 2000-2020 http://data.cma.cn/ The average annual precipitation was queried by SQL, and the Kriging interpolation was performed in ArcGIS v.10.3
Wind velocity (m/s) WIND 2000-2020 http://data.cma.cn/ The annual average wind velocity was queried by SQL, and the Kriging interpolation was performed in ArcGIS v.10.3
Topography Elevation (m) ELEV 2020 https://ladsweb.modaps.eosdis.nasa.gov/search Mosaic, masking, projection, and resampling in ArcGIS v.10.3
Aspect ASP 2020 - Extracted by aspect tool in ArcGIS v.10.3
Slope (°) SLP 2020 - Deriving slope from an elevation surface in ArcGIS v.10.3
Soil data Soil type SOL 2000 http://www.geodata.cn Masking, projection, and resampling in ArcGIS v.10.3
Vegetation Vegetation type VEG 1987 Map of vegetation types
in Inner Mongolia Autonomous Region		 Masking, projection, and resampling in ArcGIS v.10.3
Human activity Land use type LAND 2020 http://www.resdc.cn/ Masking, projection, and resampling in ArcGIS v.10.3
Population density (person/km2) POP 2000-2019 Statistical Yearbook of Xilin Gol League Joining a layer to another attribute table based on a common field in ArcGIS v.10.3
GDP per capita (CNY/person) GDP 2000-2019 Statistical Yearbook of Xilin Gol League Joining a layer to another attribute table based on a common field in ArcGIS v.10.3
Livestock number (head) LIV 2000-2019 Statistical Yearbook of Xilin Gol League Joining a layer to another attribute table based on a common field in ArcGIS v.10.3
Table 1 Research data used in this study
Fig. 2 Research framework in this study
Description Interaction Description Interaction
q(X1∩X2)<min(q(X1), q(X2)) Weaken, nonlinear q(X1∩X2)=q(X1)+q(X2) Independent
min(q(X1), q(X2)<q(X1∩X2)<max(q(X1), q(X2)) Weaken, univariate q(X1∩X2)>q(X1)+q(X2) Enhance, nonlinear
q(X1∩X2)>max(q(X1), q(X2)) Enhance, bivariate
Table 2 Interaction relationship of each factor
Fig. 3 Map of driving factors of NDVI change in the Xilin Gol grassland. (a), annual mean temperature; (b), mean annual precipitation; (c), mean wind velocity; (d), elevation; (e), aspect; (f), slope; (g), soil type; (h), vegetation type; (i), land use type; (j), population density; (k), GDP per capita; (l), livestock number.
Scenario Name Code Parameter and planning policy
Business as usual Business as usual BAU Using the seven driving factors screened in Figure 6, keeping the model parameter settings unchanged, providing no processing for the driving factors, and following the inertial development law of NDVI.
Scenario of climate change Increased wind velocity WIN+ Increasing the overall wind velocity value by 25%, while keeping other factors constant.
Reduced wind velocity WIN- Reducing the overall wind velocity value by 25%, while keeping other factors constant.
Increased precipitation PRE+ Increasing the overall precipitation value by 25%, while keeping other factors constant.
Reduced precipitation PRE- Reducing the overall precipitation value by 25%, while keeping other factors constant.
Scenario of economic priority Increased livestock number PD+ Increasing the value of livestock by 50%, while keeping other factors constant.
Scenario of ecological protection Reduced livestock number PD- Reducing the value of livestock by 50%, while keeping other factors constant.
Table 3 Parameter settings in different scenarios
Fig. 4 Inter-annual NDVI variation of the Xilin Gol grassland during 2000-2020
SNDVI Z value Trend of NDVI Area percentage (%)
S< -0.0005 Z≤ -1.64 Significant decrease 0.67
S< -0.0005 -1.64<Z<1.64 Slight decrease 6.76
-0.0005-0.0005 -1.64<Z<1.64 Stability 8.04
S≥0.0005 -1.64<Z<1.64 Slight increase 58.11
S≥0.0005 Z≥1.64 Significant increase 26.42
Table 4 Statistics of NDVI trend
Fig. 5 Trend of NDVI of the Xilin Gol grassland during 2000-2020
Fig. 6 Single factor affecting vegetation change in the Xilin Gol grassland during 2000-2020 and its q value. TEM, temperature; PRE, precipitation; WIND, wind velocity; ELEV, elevation; ASP, aspect; SLP, slope; SOL, soil type; VEG, vegetation type; LAND, land use type; POP, population density; GDP, GDP per captital; LIV, livestock number. The abbreviations are the same in Figure 7.
Fig. 7 Interactions among driving factors of NDVI
Fig. 8 Risk detection results of driving factors of mean NDVI. (a), temperature; (b), precipitation; (c), wind velocity; (d), elevation; (e), aspect; (f), slope; (g), soil type; (h), vegetation type; (i), land use type; (j), population density; (k), GDP per capita; (l), livestock number.
Factor Indicator Suitable range/type Mean NDVI
TEM Temperature (℃) 2.74-3.33 0.2262
PRE Precipitation (mm) 303.89-393.36 0.2540
WIND Wind velocity (m/s) 2.48-2.84 0.2487
ELEV Elevation (m) 1256-1695 0.2284
ASP Aspect North 0.1994
SLP Slope (°) 8.22-16.18 0.3093
SOL Soil type Gray forest soil 0.3253
VEG Vegetation type Broad-leaved forest 0.3321
LAND Land use type Forest land 0.3127
POP Population density (person/km2) 3.6-13.9 0.2262
GDP GDP per capita (CNY/person) 47,431-74,442 0.2288
LIV Livestock number (head) 1,486,331-2,570,527 0.2309
Table 5 Suitable range/type of each driving factor (95% confidence level)
Fig. 9 Real spatial distribution of NDVI in 2010 (a), 2015 (b), 2020 (c) and its simulated result in 2020 (d)
Fig. 10 Simulated spatial distribution of NDVI in the Xilin Gol grassland under different scenarios in 2030. (a), BAU; (b), WIN+; (c), WIN-; (d), PRE+; (e), PRE-; (f), PD+; (g), PD-. The detailed scenarios are presented in Table 3.
Fig. 11 Area change of NDVI at each level under different scenarios (WIN+, WIN-, PRE+, PRE-, PD+, and PD-) in 2030. The detailed scenarios are presented in Table 3.
Category Factor Method q value Category Factor Method q value
Climate Temperature Natural break 0.3333 Topography Elevation Natural break 0.0981
Quantile 0.3301 Quantile 0.111
Geometrical interval 0.2884 Geometrical interval 0.0781
Precipitation Natural break 0.5995 Slope Natural break 0.1545
Quantile 0.6262 Quantile 0.1213
Geometrical interval 0.5770 Geometrical interval 0.1232
Drought index Natural break 0.6351 Anthropogenic activities Population density Natural break 0.2305
Quantile 0.6447 Quantile 0.3188
Geometrical interval 0.5899 Geometrical interval 0.3177
Wind speed Natural break 0.6283 GDP
per capita		 Natural break 0.1256
Quantile 0.6313 Quantile 0.3242
Geometrical interval 0.6162 Geometrical interval 0.3060
Soil pH value Natural break 0.1583 Livestock Natural break 0.4491
Quantile 0.1389 Quantile 0.2921
Geometrical interval 0.1595 Geometrical interval 0.3121
Table S1 Zoning effect of geographical detector
Fig. S1 Scale effect of geographic detector result (q value and 90% quantile of q value). x1-x14 are the detailed driving factors.
C=q(X1∩X2) A=q(X1) B=q(X2) Conclusion Interpretation
x1∩x2=0.7192 0.3327 0.6190 C<A+B;C>A,B *
x1∩x3=0.7094 0.3327 0.6379 C<A+B;C>A,B *
x1∩x4=0.6955 0.3327 0.6236 C<A+B;C>A,B *
x1∩x5=0.4284 0.3327 0.0987 C<A+B;C>A,B *
x1∩x6=0.3469 0.3327 0.0051 C>A+B;C>A,B ↑
x1∩x7=0.4776 0.3327 0.1549 C<A+B;C>A,B *
x1∩x8=0.5781 0.3327 0.4635 C<A+B;C>A,B *
x1∩x9=0.4402 0.3327 0.1653 C<A+B;C>A,B *
x1∩x10=0.5063 0.3327 0.1830 C<A+B;C>A,B *
x1∩x11=0.4832 0.3327 0.1428 C<A+B;C>A,B *
x1∩x12=0.6069 0.3327 0.3220 C<A+B;C>A,B *
x1∩x13=0.6562 0.3327 0.3217 C<A+B;C>A,B *
x1∩x14=0.6323 0.3327 0.4490 C<A+B;C>A,B *
x2∩x3=0.6586 0.6190 0.6379 C<A+B;C>A,B *
x2∩x4=0.7456 0.6190 0.6236 C<A+B;C>A,B *
x2∩x5=0.6651 0.6190 0.0987 C<A+B;C>A,B *
x2∩x6=0.6285 0.6190 0.0051 C>A+B;C>A,B ↑
x2∩x7=0.6529 0.6190 0.1549 C<A+B;C>A,B *
x2∩x8=0.7063 0.6190 0.4635 C<A+B;C>A,B *
x2∩x9=0.6863 0.6190 0.1653 C<A+B;C>A,B *
x2∩x10=0.6597 0.6190 0.1830 C<A+B;C>A,B *
x2∩x11=0.6699 0.6190 0.1428 C<A+B;C>A,B *
x2∩x12=0.6754 0.6190 0.3220 C<A+B;C>A,B *
x2∩x13=0.7133 0.6190 0.3217 C<A+B;C>A,B *
x2∩x14=0.7182 0.6190 0.4490 C<A+B;C>A,B *
x3∩x4=0.7179 0.6379 0.6236 C<A+B;C>A,B *
x3∩x5=0.6690 0.6379 0.0987 C<A+B;C>A,B *
x3∩x6=0.6488 0.6379 0.0051 C>A+B;C>A,B *
x3∩x7=0.6856 0.6379 0.1549 C<A+B;C>A,B *
x3∩x8=0.7272 0.6379 0.4635 C<A+B;C>A,B *
x3∩x9=0.6919 0.6379 0.1653 C<A+B;C>A,B *
x3∩x10=0.6936 0.6379 0.1830 C<A+B;C>A,B *
x3∩x11=0.7019 0.6379 0.1428 C<A+B;C>A,B *
x3∩x12=0.6754 0.6379 0.3220 C<A+B;C>A,B *
x3∩x13=0.6878 0.6379 0.3217 C<A+B;C>A,B *
x3∩x14=0.6909 0.6379 0.4490 C<A+B;C>A,B *
x4∩x5=0.7112 0.6236 0.0987 C<A+B;C>A,B *
x4∩x6=0.6343 0.6236 0.0051 C>A+B;C>A,B ↑
x4∩x7=0.6696 0.6236 0.1549 C<A+B;C>A,B *
x4∩x8=0.7144 0.6236 0.4635 C<A+B;C>A,B *
x4∩x9=0.6553 0.6236 0.1653 C<A+B;C>A,B *
x4∩x10=0.6775 0.6236 0.1830 C<A+B;C>A,B *
x4∩x11=0.6844 0.6236 0.1428 C<A+B;C>A,B *
x4∩x12=0.6893 0.6236 0.3220 C<A+B;C>A,B *
x4∩x13=0.6800 0.6236 0.3217 C<A+B;C>A,B *
x4∩x14=0.6414 0.6236 0.4490 C<A+B;C>A,B *
x5∩x6=0.1131 0.0987 0.0051 C>A+B;C>A,B ↑
x5∩x7=0.2682 0.0987 0.1549 C<A+B;C>A,B *
x5∩x8=0.5320 0.0987 0.4635 C<A+B;C>A,B *
x5∩x9=0.2834 0.0987 0.1653 C<A+B;C>A,B *
x5∩x10=0.2655 0.0987 0.1830 C<A+B;C>A,B *
x5∩x11=0.2289 0.0987 0.1428 C<A+B;C>A,B *
x5∩x12=0.3949 0.0987 0.3220 C<A+B;C>A,B *
x5∩x13=0.5603 0.0987 0.3217 C<A+B;C>A,B *
x5∩x14=0.5644 0.0987 0.4490 C<A+B;C>A,B *
x6∩x7=0.1691 0.0051 0.1549 C>A+B;C>A,B ↑
x6∩x8=0.4859 0.0051 0.4635 C>A+B;C>A,B ↑
x6∩x9=0.1916 0.0051 0.1653 C>A+B;C>A,B ↑
x6∩x10=0.2029 0.0051 0.1830 C>A+B;C>A,B ↑
x6∩x11=0.1590 0.0051 0.1428 C>A+B;C>A,B ↑
x6∩x12=0.3342 0.0051 0.3220 C>A+B;C>A,B ↑
x6∩x13=0.3348 0.0051 0.3217 C>A+B;C>A,B ↑
x6∩x14=0.4654 0.0051 0.4490 C>A+B;C>A,B ↑
x7∩x8=0.5047 0.1549 0.4635 C<A+B;C>A,B *
x7∩x9=0.2882 0.1549 0.1653 C<A+B;C>A,B *
x7∩x10=0.2823 0.1549 0.1830 C<A+B;C>A,B *
x7∩x11=0.2424 0.1549 0.1428 C<A+B;C>A,B *
x7∩x12=0.4222 0.1549 0.3220 C<A+B;C>A,B *
x7∩x13=0.4106 0.1549 0.3217 C<A+B;C>A,B *
x7∩x14=0.5165 0.1549 0.4490 C<A+B;C>A,B *
x8∩x9=0.5402 0.4635 0.1653 C<A+B;C>A,B *
x8∩x10=0.5350 0.4635 0.1830 C<A+B;C>A,B *
x8∩x11=0.5375 0.4635 0.1428 C<A+B;C>A,B *
x8∩x12=0.6301 0.4635 0.3220 C<A+B;C>A,B *
x8∩x13=0.5966 0.4635 0.3217 C<A+B;C>A,B *
x8∩x14=0.6628 0.4635 0.4490 C<A+B;C>A,B *
x9∩x10=0.3169 0.1653 0.1830 C<A+B;C>A,B *
x9∩x11=0.2952 0.1653 0.1428 C<A+B;C>A,B *
x9∩x12=0.4319 0.1653 0.3220 C<A+B;C>A,B *
x9∩x13=0.4137 0.1653 0.3217 C<A+B;C>A,B *
x9∩x14=0.5422 0.1653 0.4490 C<A+B;C>A,B *
x10∩x11=0.2631 0.1830 0.1428 C<A+B;C>A,B *
x10∩x12=0.4232 0.1830 0.3220 C<A+B;C>A,B *
x10∩x13=0.4587 0.1830 0.3217 C<A+B;C>A,B *
x10∩x14=0.5300 0.1830 0.4490 C<A+B;C>A,B *
x11∩x12=0.4188 0.1428 0.3220 C<A+B;C>A,B *
x11∩x13=0.4380 0.1428 0.3217 C<A+B;C>A,B *
x11∩x14=0.5287 0.1428 0.4490 C<A+B;C>A,B *
x12∩x13=0.6098 0.3220 0.3217 C<A+B;C>A,B *
x12∩x14=0.6103 0.3220 0.4490 C<A+B;C>A,B *
x13∩x14=0.5671 0.3217 0.4490 C<A+B;C>A,B *
Table S2 Interaction of driver factor
NDVI type Very low Low Medium High Very high
Very low 48,087 25,769 4551 310 31
Low 1412 12,212 13,617 3705 326
Medium 113 2987 15,283 12,800 2621
High 22 342 5196 14,269 8505
Very high 21 33 576 5160 23,856
Table S3 Conversion matrix of NDVI
NDVI type Very low Low Medium High Very high
Very low 0.610647 0.327237 0.057788 0.003937 0.000391
Low 0.045158 0.390517 0.435443 0.118467 0.010415
Medium 0.003339 0.088354 0.452113 0.378662 0.077532
High 0.000768 0.012079 0.183370 0.503620 0.300163
Very high 0.000706 0.001105 0.019444 0.174058 0.804688
Table S4 Conversion probability of NDVI
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